System and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles

ABSTRACT

A system and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to a normal driving behavior safe zone; receiving a proposed vehicle control command; comparing the proposed vehicle control command with the normal driving behavior safe zone; and issuing a warning alert if the proposed vehicle control command is outside of the normal driving behavior safe zone. Another embodiment includes modifying the proposed vehicle control command to produce a modified and validated vehicle control command if the proposed vehicle control command is outside of the normal driving behavior safe zone.

PRIORITY PATENT APPLICATION

This patent application is a continuation patent application drawingpriority from U.S. non-provisional patent application Ser. No.15/640,521; filed Jul. 1, 2017. This present non-provisional patentapplication draws priority from the referenced patent application. Theentire disclosure of the referenced patent application is consideredpart of the disclosure of the present application and is herebyincorporated by reference herein in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the disclosure hereinand to the drawings that form a part of this document: Copyright2016-2019, TuSimple, All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatuses,methodologies, computer program products, etc.) for vehicle controlsystems, autonomous driving systems, vehicle control command generation,and more particularly, but not by way of limitation, to a system andmethod for using human driving patterns to detect and correct abnormaldriving behaviors of autonomous vehicles.

BACKGROUND

An autonomous vehicle is often configured and controlled to follow atrajectory based on a computed driving path. However, when variablessuch as obstacles are present on the driving path, the autonomousvehicle must perform control operations so that the vehicle may besafely driven by changing the driving path in real time. The autonomousdriving system or control system of the vehicle must make these controladjustments to cause the vehicle to follow the desired trajectory andavoid obstacles. During the process of developing and testing autonomousdriving systems, bugs or mistakes from the autonomous vehicle controlsystem might cause dangerous or abnormal maneuvers of the vehicle. Thesedangerous or abnormal maneuvers can jeopardize the occupants of theautonomous vehicle and other people and property in proximity to theautonomous vehicle.

SUMMARY

A system and method for using human driving patterns to detect andcorrect abnormal driving behaviors of autonomous vehicles are disclosedherein. The various example embodiments described herein provide asystem and method to detect dangerous or abnormal driving maneuvers andto guard the passengers against such situations. An example embodimentfirst trains a learning module or a normal human driving behavior modelwith normal driving maneuvers from human driving data. Then, the exampleembodiment provides a way to measure the distance between the currentautonomous driving control process driving command and the correspondingnormal human maneuver patterns from the learning module. If the distanceis above a preset threshold, the system and method intercept and reduceor truncate the dangerous or abnormal maneuver of the current autonomousdriving control process driving command to a preset safe bound and sendout a warning.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of an example ecosystem in which avehicle control module of an example embodiment can be implemented;

FIG. 2 illustrates the components of the vehicle control system of anexample embodiment;

FIG. 3 illustrates a plot of typical human driver behavior data in ahigh dimensional space;

FIG. 4 illustrates a plot of typical human driver behavior data in a lowdimensional space;

FIG. 5 illustrates a boundary or minimum bounding box drawn around acluster of typical human driver behavior data;

FIGS. 6 and 7 illustrate a process in an example embodiment forcomparing a proposed vehicle control command with a normal drivingbehavior safe zone to determine if the proposed vehicle control commandis within the normal driving behavior safe zone;

FIG. 8 illustrates an example embodiment of a system and method forusing human driving patterns to detect and correct abnormal drivingbehaviors of autonomous vehicles;

FIG. 9 is a process flow diagram illustrating an example embodiment of asystem and method for using human driving patterns to detect and correctabnormal driving behaviors of autonomous vehicles; and

FIG. 10 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a system and method forusing normal human driving patterns to detect and correct abnormaldriving behaviors of autonomous vehicles are described herein. Anexample embodiment disclosed herein can be used in the context of anin-vehicle control system 150 in a vehicle ecosystem 101. In one exampleembodiment, an in-vehicle control system 150 with a vehicle controlmodule 200 resident in a vehicle 105 can be configured like thearchitecture and ecosystem 101 illustrated in FIG. 1. However, it willbe apparent to those of ordinary skill in the art that the vehiclecontrol module 200 described and claimed herein can be implemented,configured, and used in a variety of other applications and systems aswell.

Referring now to FIG. 1, a block diagram illustrates an exampleecosystem 101 in which an in-vehicle control system 150 and a vehiclecontrol module 200 of an example embodiment can be implemented. Thesecomponents are described in more detail below. Ecosystem 101 includes avariety of systems and components that can generate and/or deliver oneor more sources of information/data and related services to thein-vehicle control system 150 and the vehicle control module 200, whichcan be installed in the vehicle 105. For example, a camera installed inthe vehicle 105, as one of the devices of vehicle subsystems 140, cangenerate image and timing data that can be received by the in-vehiclecontrol system 150. The in-vehicle control system 150 and an imageprocessing module executing therein can receive this image and timingdata input. The image processing module can extract object data from theimage and timing data to identify objects in the proximity of thevehicle. The in-vehicle control system 150 can process the object dataand generate a trajectory for the vehicle based on the detected objects.The trajectory can be used by an autonomous vehicle control subsystem,as another one of the subsystems of vehicle subsystems 140. In anexample embodiment, the in-vehicle control system 150 can generate avehicle control command signal, which can be used by a subsystem ofvehicle subsystems 140 to cause the vehicle to traverse the generatedtrajectory. The autonomous vehicle control subsystem, for example, canuse the real-time generated trajectory and vehicle control commandsignal to safely and efficiently navigate the vehicle 105 through a realworld driving scenario while avoiding obstacles and safely controllingthe vehicle.

In an example embodiment as described herein, the in-vehicle controlsystem 150 can be in data communication with a plurality of vehiclesubsystems 140, all of which can be resident in a user's vehicle 105. Avehicle subsystem interface 141 is provided to facilitate datacommunication between the in-vehicle control system 150 and theplurality of vehicle subsystems 140. The in-vehicle control system 150can be configured to include a data processor 171 to execute the vehiclecontrol module 200 for processing data received from one or more of thevehicle subsystems 140. The data processor 171 can be combined with adata storage device 172 as part of a computing system 170 in thein-vehicle control system 150. The data storage device 172 can be usedto store data, processing parameters, and data processing instructions.A processing module interface 165 can be provided to facilitate datacommunications between the data processor 171 and the vehicle controlmodule 200. In various example embodiments, a plurality of processingmodules, configured similarly to vehicle control module 200, can beprovided for execution by data processor 171. As shown by the dashedlines in FIG. 1, the vehicle control module 200 can be integrated intothe in-vehicle control system 150, optionally downloaded to thein-vehicle control system 150, or deployed separately from thein-vehicle control system 150.

The in-vehicle control system 150 can be configured to receive ortransmit data from/to a wide-area network 120 and network resources 122connected thereto. An in-vehicle web-enabled device 130 and/or a usermobile device 132 can be used to communicate via network 120. Aweb-enabled device interface 131 can be used by the in-vehicle controlsystem 150 to facilitate data communication between the in-vehiclecontrol system 150 and the network 120 via the in-vehicle web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe in-vehicle control system 150 to facilitate data communicationbetween the in-vehicle control system 150 and the network 120 via theuser mobile device 132. In this manner, the in-vehicle control system150 can obtain real-time access to network resources 122 via network120. The network resources 122 can be used to obtain processing modulesfor execution by data processor 171, data content to train internalneural networks, system parameters, or other data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as websites, servers, central control sites,or the like. The network resources 122 can generate and/or distributedata, which can be received in vehicle 105 via in-vehicle web-enableddevices 130 or user mobile devices 132. The network resources 122 canalso host network cloud services, which can support the functionalityused to compute or assist in processing object input or object inputanalysis. Antennas can serve to connect the in-vehicle control system150 and the vehicle control module 200 with the data network 120 viacellular, satellite, radio, or other conventional signal receptionmechanisms. Such cellular data networks are currently available (e.g.,Verizon™, AT&T™, T-Mobile™, etc.). Such satellite-based data or contentnetworks are also currently available (e.g., SiriusXM™, HughesNet™,etc.). The conventional broadcast networks, such as AM/FM radionetworks, pager networks, UHF networks, gaming networks, WiFi networks,peer-to-peer networks, Voice over IP (VoIP) networks, and the like arealso well-known. Thus, as described in more detail below, the in-vehiclecontrol system 150 and the vehicle control module 200 can receiveweb-based data or content via an in-vehicle web-enabled device interface131, which can be used to connect with the in-vehicle web-enabled devicereceiver 130 and network 120. In this manner, the in-vehicle controlsystem 150 and the vehicle control module 200 can support a variety ofnetwork-connectable in-vehicle devices and systems from within a vehicle105.

As shown in FIG. 1, the in-vehicle control system 150 and the vehiclecontrol module 200 can also receive data, processing control parameters,and training content from user mobile devices 132, which can be locatedinside or proximately to the vehicle 105. The user mobile devices 132can represent standard mobile devices, such as cellular phones,smartphones, personal digital assistants (PDA's), MP3 players, tabletcomputing devices (e.g., iPad™), laptop computers, CD players, and othermobile devices, which can produce, receive, and/or deliver data,processing control parameters, and content for the in-vehicle controlsystem 150 and the vehicle control module 200. As shown in FIG. 1, themobile devices 132 can also be in data communication with the networkcloud 120. The mobile devices 132 can source data and content frominternal memory components of the mobile devices 132 themselves or fromnetwork resources 122 via network 120. Additionally, mobile devices 132can themselves include a GPS data receiver, accelerometers, WiFitriangulation, or other geo-location sensors or components in the mobiledevice, which can be used to determine the real-time geo-location of theuser (via the mobile device) at any moment in time. In any case, thein-vehicle control system 150 and the vehicle control module 200 canreceive data from the mobile devices 132 as shown in FIG. 1.

Referring still to FIG. 1, the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the in-vehicle controlsystem 150 via vehicle subsystem interface 141 may include informationabout the state of one or more of the components or subsystems of thevehicle 105. In particular, the data signals, which can be communicatedfrom the vehicle operational subsystems 140 to a Controller Area Network(CAN) bus of the vehicle 105, can be received and processed by thein-vehicle control system 150 via vehicle subsystem interface 141.Embodiments of the systems and methods described herein can be used withsubstantially any mechanized system that uses a CAN bus or similar datacommunications bus as defined herein, including, but not limited to,industrial equipment, boats, trucks, machinery, or automobiles; thus,the term “vehicle” as used herein can include any such mechanizedsystems. Embodiments of the systems and methods described herein canalso be used with any systems employing some form of network datacommunications; however, such network communications are not required.

Referring still to FIG. 1, the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its environment, determine a predicted behavior of at least oneother vehicle in the environment, determine a confidence level that maycorrespond to a likelihood of the at least one other vehicle to performthe predicted behavior, and control the vehicle 105 based on thedetermined information. While in autonomous mode, the vehicle 105 may beconfigured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the in-vehicle control system 150, thecomputing system 170, and the vehicle control module 200. The vehicle105 may include more or fewer subsystems and each subsystem couldinclude multiple elements. Further, each of the subsystems and elementsof vehicle 105 could be interconnected. Thus, one or more of thedescribed functions of the vehicle 105 may be divided up into additionalfunctional or physical components or combined into fewer functional orphysical components. In some further examples, additional functional andphysical components may be added to the examples illustrated by FIG. 1.

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The wheels may represent at least one wheel that is fixedlyattached to the transmission and at least one tire coupled to a rim ofthe wheel that could make contact with the driving surface. The wheelsmay include a combination of metal and rubber, or another combination ofmaterials. The transmission may include elements that are operable totransmit mechanical power from the engine to the wheels. For thispurpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information about an environment or condition of thevehicle 105. For example, the vehicle sensor subsystem 144 may includean inertial measurement unit (IMU), a Global Positioning System (GPS)transceiver, a RADAR unit, a laser range finder/LIDAR unit, and one ormore cameras or image capture devices. The vehicle sensor subsystem 144may also include sensors configured to monitor internal systems of thevehicle 105 (e.g., an 02 monitor, a fuel gauge, an engine oiltemperature). Other sensors are possible as well. One or more of thesensors included in the vehicle sensor subsystem 144 may be configuredto be actuated separately or collectively in order to modify a position,an orientation, or both, of the one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The RADAR unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the RADAR unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit may be anysensor configured to sense objects in the environment in which thevehicle 105 is located using lasers. In an example embodiment, the laserrange finder/LIDAR unit may include one or more laser sources, a laserscanner, and one or more detectors, among other system components. Thelaser range finder/LIDAR unit could be configured to operate in acoherent (e.g., using heterodyne detection) or an incoherent detectionmode. The cameras may include one or more devices configured to capturea plurality of images of the environment of the vehicle 105. The camerasmay be still image cameras or motion video cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit.

The steering unit may represent any combination of mechanisms that maybe operable to adjust the heading of vehicle 105. The throttle may beconfigured to control, for instance, the operating speed of the engineand, in turn, control the speed of the vehicle 105. The brake unit caninclude any combination of mechanisms configured to decelerate thevehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the vehicle control module 200, the GPS transceiver, and one ormore predetermined maps so as to determine the driving path for thevehicle 105. The autonomous control unit may represent a control systemconfigured to identify, evaluate, and avoid or otherwise negotiatepotential obstacles in the environment of the vehicle 105. In general,the autonomous control unit may be configured to control the vehicle 105for operation without a driver or to provide driver assistance incontrolling the vehicle 105. In some embodiments, the autonomous controlunit may be configured to incorporate data from the vehicle controlmodule 200, the GPS transceiver, the RADAR, the LIDAR, the cameras, andother vehicle subsystems to determine the driving path or trajectory forthe vehicle 105. The vehicle control system 146 may additionally oralternatively include components other than those shown and described.

Occupant interface subsystems 148 may be configured to allow interactionbetween the vehicle 105 and external sensors, other vehicles, othercomputer systems, and/or an occupant or user of vehicle 105. Forexample, the occupant interface subsystems 148 may include standardvisual display devices (e.g., plasma displays, liquid crystal displays(LCDs), touchscreen displays, heads-up displays, or the like), speakersor other audio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, means for a user/occupant of the vehicle 105 tointeract with the other vehicle subsystems. The visual display devicesmay provide information to a user of the vehicle 105. The user interfacedevices can also be operable to accept input from the user via atouchscreen. The touchscreen may be configured to sense at least one ofa position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providemeans for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 140, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as data processing parameters, training data, humandriving model data, human driving model parameters, roadway maps, andpath information, among other information. Such information may be usedby the vehicle 105 and the computing system 170 during the operation ofthe vehicle 105 in the autonomous, semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 140, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by the vehicle sensor subsystem 144 andfollow a path or trajectory generated by the vehicle control module 200.In an example embodiment, the computing system 170 can be operable toprovide control over many aspects of the vehicle 105 and its subsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, andvehicle control module 200, as being integrated into the vehicle 105,one or more of these components could be mounted or associatedseparately from the vehicle 105. For example, data storage device 172could, in part or in full, exist separate from the vehicle 105. Thus,the vehicle 105 could be provided in the form of device elements thatmay be located separately or together. The device elements that make upvehicle 105 could be communicatively coupled together in a wired orwireless fashion.

Additionally, other data and/or content (denoted herein as ancillarydata) can be obtained from local and/or remote sources by the in-vehiclecontrol system 150 as described above. The ancillary data can be used toaugment, modify, or train the operation of the vehicle control module200 based on a variety of factors including, the context in which theuser is operating the vehicle (e.g., the location of the vehicle, thespecified destination, direction of travel, speed, the time of day, thestatus of the vehicle, etc.), and a variety of other data obtainablefrom the variety of sources, local and remote, as described herein.

In a particular embodiment, the in-vehicle control system 150 and thevehicle control module 200 can be implemented as in-vehicle componentsof vehicle 105. In various example embodiments, the in-vehicle controlsystem 150 and the vehicle control module 200 in data communicationtherewith can be implemented as integrated components or as separatecomponents. In an example embodiment, the software components of thein-vehicle control system 150 and/or the vehicle control module 200 canbe dynamically upgraded, modified, and/or augmented by use of the dataconnection with the mobile devices 132 and/or the network resources 122via network 120. The in-vehicle control system 150 can periodicallyquery a mobile device 132 or a network resource 122 for updates orupdates can be pushed to the in-vehicle control system 150.

Referring now to FIG. 2, a diagram illustrates the components of avehicle control system 201 with the vehicle control module 200 of anexample embodiment. In the example embodiment, the vehicle controlmodule 200 can be configured to include an abnormal command detectionmodule 173 and a human driving model module 175. As described in moredetail below, the abnormal command detection module 173 and the humandriving model module 175 serve to enable the detection and correction ofan abnormal vehicle control command for the vehicle based on acomparison of a proposed vehicle control command 210 with correspondingnormal human driving behavior data maintained by the human driving modelmodule 175. The abnormal command detection module 173 and the humandriving model module 175 can be configured as software modules executedby the data processor 171 of the in-vehicle control system 150. Themodules 173 and 175 of the vehicle control module 200 can receive aproposed vehicle control command 210 and produce a validated or modifiedvehicle control command 220, which can be used by the autonomous controlsubsystem of the vehicle control subsystem 146 to efficiently and safelycontrol the vehicle 105. As part of their abnormal command detection andcorrection processing, the abnormal command detection module 173 and thehuman driving model module 175 can be configured to work with humandriving model parameters 174, which can be used to customize and finetune the operation of the vehicle control module 200. The human drivingmodel parameters 174 can be stored in a memory 172 of the in-vehiclecontrol system 150.

In the example embodiment, the vehicle control module 200 can beconfigured to include an interface with the in-vehicle control system150, as shown in FIG. 1, through which the vehicle control module 200can send and receive data as described herein. Additionally, the vehiclecontrol module 200 can be configured to include an interface with thein-vehicle control system 150 and/or other ecosystem 101 subsystemsthrough which the vehicle control module 200 can receive ancillary datafrom the various data sources described above. As described above, thevehicle control module 200 can also be implemented in systems andplatforms that are not deployed in a vehicle and not necessarily used inor with a vehicle.

In an example embodiment as shown in FIG. 2, the vehicle control module200 can be configured to include the abnormal command detection module173 and the human driving model module 175, as well as other processingmodules not shown for clarity. Each of these modules can be implementedas software, firmware, or other logic components executing or activatedwithin an executable environment of the vehicle control module 200operating within or in data communication with the in-vehicle controlsystem 150. Each of these modules of an example embodiment is describedin more detail below in connection with the figures provided herein.

System and Method for Using Human Driving Patterns to Detect and CorrectAbnormal Driving Behaviors of Autonomous Vehicles

A system and method for using human driving patterns to detect andcorrect abnormal driving behaviors of autonomous vehicles are disclosedherein. The various example embodiments described herein provide asystem and method to detect dangerous or abnormal driving maneuvers andto guard the passengers against such situations. An example embodimentfirst trains a learning module or a normal human driving behavior modelwith normal driving maneuvers from human driving data. Then, the exampleembodiment provides a way to measure the distance between the currentautonomous driving control process driving command and the correspondingnormal human maneuver patterns from the learning module. If the distanceis above a preset threshold, the system and method intercept and reduceor truncate the dangerous or abnormal maneuver of the current autonomousdriving control process driving command to a preset safe bound and sendout a warning.

An example embodiment can develop a human driving behavior model basedon data related to various types of driving behaviors captured andretained by the human driving model module 175 of the exampleembodiment. The example embodiment can use actual empirical datacaptured through vehicle sensor subsystems and driving simulation datato model typical human driving behaviors. This empirical data andsimulation data is captured and used by the human driving model module175 to encode data corresponding to these typical driving behaviors asmathematical or data representations. The data can be encoded as aneural network, rules sets, or other well-known methods for developingmachine learning systems. The empirical data can be captured for asingle vehicle and/or aggregated from data collected from a largepopulation of vehicles and drivers. Over time, the human driving modelmodule 175 can learn typical driving behaviors, identify and retaindriving behaviors deemed normal and safe, and expunge behaviors deemedunsafe or residing outside common operational thresholds.

For example, an embodiment can learn a common human driving behavior,such as one related to steering an autonomous vehicle and/or passing anobstacle (e.g., another vehicle) in the roadway. The human driving modelmodule 175 can receive empirical data and simulation data related todriving behaviors that correspond to a steering angle applied to thesteering controls of the vehicle as a function of time. Abrupt,swerving, or unsafe turn rates, indicated by steep steering angleslopes, can be detected and expunged from the human driving model.Typically, when a vehicle is driven by human drivers and the driverperforms a left-side or right-side passing maneuver, the relationshipbetween the steering angle and time can be learned and retained as asmooth data curve and a corresponding function by the human drivingmodel module 175. As such, data corresponding to these steering andpassing behaviors can be received, retained as a mathematical or datarepresentation, and learned by the human driving model module 175 of anexample embodiment.

An example embodiment can also learn a common driving behavior relatedto accelerating or decelerating an autonomous vehicle and/or managingthe speed of the vehicle. The human driving model module 175 can receiveempirical data and simulation data related to driving behaviors thatcorrespond to a throttle level or throttle percentage applied to theengine or drivetrain controls of the vehicle as a function of time. Aninitial increase in the throttle percentage for a period of time canindicate an accelerating or vehicle speed increase behavior as typicalwhen a vehicle passes an obstacle, such as another vehicle on theroadway. The slope of the throttle percentage indicates the typical rateof acceleration for this type of driving behavior. Abrupt or unsafeacceleration rates, indicated by steep throttle percentage slopes, canbe detected and expunged from the human driving model. In acorresponding fashion, a decelerating throttle percentage for a periodof time can indicate a decelerating action or a vehicle speed decreasebehavior. Abrupt or unsafe deceleration rates, indicated by steepthrottle percentage slopes, can be detected and expunged from the humandriving model. Typically, when a vehicle is driven by human drivers andthe driver performs an acceleration or deceleration maneuver, therelationship between the throttle percentage and time can be learned andretained as a smooth data curve and a corresponding function by thehuman driving model module 175. As such, data corresponding to theseacceleration or deceleration behaviors can be received, retained as amathematical or data representation, and learned by the human drivingmodel module 175 of an example embodiment.

An example embodiment can also learn a common driving behavior relatedto braking or stopping an autonomous vehicle and/or managing the speedof the vehicle. The human driving model module 175 can receive empiricaldata and simulation data related to driving behaviors that correspond toa braking level or braking percentage applied to the braking controls ofthe vehicle as a function of time. An initial increase in the brakingpercentage for a period of time can indicate a vehicle stopping behavioras typical when a driver depresses the brake pedal. The slope of thebraking percentage indicates the typical rate of braking for this typeof driving behavior. Abrupt or unsafe braking rates, indicated by steepbraking percentage slopes, can be detected and expunged from the humandriving model. In a corresponding fashion, a reduced or decreasingbraking percentage for a period of time can indicate a reduced vehiclebraking behavior. Typically, when a vehicle is driven by human driversand the driver performs a braking maneuver, the relationship between thebraking percentage and time can be learned and retained as a smooth datacurve and a corresponding function by the human driving model module175. As such, data corresponding to these braking behaviors can bereceived, retained as a mathematical or data representation, and learnedby the human driving model module 175 of an example embodiment.

As described above, the human driving model module 175 of an exampleembodiment can develop a human driving behavior model based on datarelated to various types of driving behaviors. This data can beaggregated over many drivers, vehicles, driving scenarios, and drivingconditions. Sensor data for the each of the vehicles being used to trainthe human driving behavior model can be captured and plotted on a graph,such as the graph shown in FIG. 3. FIG. 3 illustrates a plot of humandriver behavior data based on sensor data received over time fromsensors in each of a multitude of vehicles being used to train the humandriving behavior model. The data plotted on the graph shown in FIG. 3can also include data from a vehicle driving simulation system. Thegraph shown in FIG. 3 illustrates a plot of typical aggregated humandriver behavior based on sensor data in a high dimensional space,wherein sensor data from a plurality of sensors is plotted as a functionof time. The disbursement and dimensionality of this plotted data can bereduced by plotting or projecting the aggregated human driver behaviordata into a low dimensional space, wherein sensor data from theplurality of sensors is projected. Data mining techniques can be used toperform this projection of the aggregated human driver behavior sensordata from a high dimensional space to a low dimensional space. Ingeneral, only data with useful and important information are clearlyobserved in the low dimensional space. An example of the projection oftypical human driver behavior data into a low dimensional space isillustrated in FIG. 4. The projected sensor data shown in FIG. 4illustrates a disbursed clustering effect produced by typical or normaldriving behaviors that tend to follow consistent trends or patterns. Inother words, normal or typical drivers tend to exhibit similar drivingbehaviors. The projected data shown in FIG. 4 illustrates a relativelyconsistent disbursed clustering effect produced by the typical or normaldriving behaviors that tend to follow consistent trends or patterns. Assuch, the projected cluster of typical human driver behavior data asshown in FIG. 4 tends to define one or more point clouds ormulti-dimensional shapes, which can be shown as two-dimensional shapesfor clarity and simplicity. For the purpose of illustration, one of thefour point clouds shown in the example of FIG. 4 is isolated and zoomedin to produce the point cloud shown in FIG. 5. Thus, FIG. 5 illustratesa point cloud or multi-dimensional shape representing sensor datacorresponding to typical human driver behavior. As shown in FIG. 5, aboundary or minimum bounding box 505 can be drawn around the cluster oftypical human driver behavior data. The interior region of this boundingbox 505 represents the data corresponding to normal, typical, or safedriving behaviors and represents a normal driving behavior safe zone510. Data corresponding to driving behaviors that do not reside withinthe bounding box 505 can be considered abnormal, atypical, or unsafedriving behaviors. As described in more detail below, this normaldriving behavior safe zone 510 can be used to detect and correctabnormal, atypical, or unsafe driving behaviors in autonomous vehicles.

Referring now to FIG. 6, an example embodiment includes a process forcomparing a proposed vehicle control command 210 with the normal drivingbehavior safe zone 510 to determine if the proposed vehicle controlcommand 210 is within the normal driving behavior safe zone 510. Duringthe process of controlling the movement of the vehicle 105, thein-vehicle control subsystem 150 can issue many control commands to thevehicle control subsystems 146 to perform a variety of driving behaviorsor maneuvers. Prior to actually commanding the vehicle controlsubsystems 146 to perform a particular maneuver, the in-vehicle controlsubsystem 150 can issue a proposed vehicle control command 210 to thevehicle control module 200. As described in more detail below, thevehicle control module 200, and the abnormal command detection module173 therein, can compare the proposed vehicle control command 210 withthe normal driving behavior safe zone 510 to determine if the proposedvehicle control command 210 is within the normal driving behavior safezone 510. In other words, the abnormal command detection module 173 candetermine if the proposed vehicle control command 210 is a normal,typical, and safe control command.

Referring again to FIG. 6, the normal driving behavior safe zone 510, asgenerated in the manner described above, is shown. The interior of thenormal driving behavior safe zone 510 represents data associated withnormal, typical, and safe vehicle control commands. In an exampleembodiment, the in-vehicle control subsystem 150 might issue a proposedvehicle control command 210 to the abnormal command detection module173, for which data plotted on the graph shown in FIG. 6 might result ina point 515 positioned as shown. The point 515 represents a location onthe graph that corresponds to the sensor data that would result from thevehicle control manipulations defined by the proposed vehicle controlcommand 210. The abnormal command detection module 173 can plot theproposed vehicle control command 210 on the graph as shown in FIG. 6. Asdescribed above, the human driving model module 175 can also plot thenormal driving behavior safe zone 510 on the graph as shown in FIG. 6.

Referring now to FIG. 7, the abnormal command detection module 173 hasplotted the proposed vehicle control command 210 on the graph as point515. The abnormal command detection module 173 can determine a distancefrom the point 515, corresponding to the proposed vehicle controlcommand 210, to a nearest point 715 on or within the normal drivingbehavior safe zone 510. A tangent line 712 perpendicular to a distancevector 710 and tangential to the boundary of the normal driving behaviorsafe zone 510 can be used to find the nearest point 715 on the boundingbox 505 of the normal driving behavior safe zone 510. The point 715represents the closest or nearest point of the normal driving behaviorsafe zone 510 to the proposed vehicle control command 210 at point 515.As such, the point 715 represents a normal or safe vehicle controlcommand that is most similar to the proposed vehicle control command210. The distance from point 515 to point 715 is defined by the distancevector 710. If the distance from point 515 to point 715 is not greaterthan zero (i.e., the point 515 is located on or within the normaldriving behavior safe zone 510), the abnormal command detection module173 has detected a normal or safe proposed vehicle control command 210.In this case, the abnormal command detection module 173 can validate theproposed vehicle control command 210 and pass the validated vehiclecontrol command 220 on to the vehicle control subsystems 146 forexecution. Conversely, if the distance from point 515 to point 715 isgreater than zero (i.e., the point 515 is not located on or within thenormal driving behavior safe zone 510), the abnormal command detectionmodule 173 has detected an abnormal or unsafe proposed vehicle controlcommand 210. In one embodiment, the abnormal command detection module173 can simply reject or invalidate the proposed vehicle control command210 and issue an error or warning alert message. In another embodiment,the abnormal command detection module 173 can actively modify the unsafeproposed vehicle control command 210 to produce a safe vehicle controlcommand that is most similar to the unsafe proposed vehicle controlcommand 210. The active modification of the unsafe proposed vehiclecontrol command 210 can be accomplished by truncating or replacing theunsafe proposed vehicle control command 210 with a modified andvalidated vehicle control command 220 corresponding to the point 715,which is the closest point in the normal driving behavior safe zone 510to the proposed vehicle control command 210. As such, the modifiedvehicle control command 220 will represent a normal or safe vehiclecontrol command that is most similar to the unsafe proposed vehiclecontrol command 210. In an example embodiment, the abnormal commanddetection module 173 can also issue an error or warning alert messagewhen the unsafe proposed vehicle control command 210 is modified asdescribed herein. The modified and validated vehicle control command 220can be passed on to the vehicle control subsystems 146 for execution. Assuch, the abnormal command detection module 173 can ensure that a normaland safe vehicle control command is always sent to the vehicle controlsubsystems 146 for execution and that an abnormal or unsafe controlcommand is never sent to the vehicle control subsystems 146 forexecution. As a result, the operation of the vehicle control module 200as described herein provides an assurance that the vehicle 105 is alwayscontrolled in a normal and safe manner.

Referring now to FIG. 8, a process diagram illustrates an exampleembodiment of a system and method for detection and correction of anabnormal vehicle control command. As shown, an example embodiment can beconfigured to use the human driving model module 175 to process humandriving data captured from a plurality of sensor components on amultitude of vehicles for which training data is retained. As describedin regard to FIG. 3, the aggregated human driving behavior sensor datacan be plotted on a graph in a high dimensional space with a pluralityof sensor data points (operation block 801 shown in FIG. 8). Asdescribed in regard to FIG. 4, the disbursement and dimensionality ofthis plotted data can be reduced by plotting or projecting theaggregated human driver behavior data into a low dimensional space,wherein sensor data from the plurality of sensors is projected(operation block 803 shown in FIG. 8). As described in regard to FIG. 5,a boundary or minimum bounding box can be drawn around the cluster oftypical human driver behavior data to produce a normal driving behaviorsafe zone (operation block 805 shown in FIG. 8). Once the data and graphcorresponding to the normal driving behavior safe zone is generated bythe human driving model module 175, the abnormal command detectionmodule 173 can begin to receive proposed vehicle control commands 210.In operation block 807 shown in FIG. 8 and as detailed in regard toFIGS. 6 and 7, the abnormal command detection module 173 can compare theproposed vehicle control command 210 with the normal driving behaviorsafe zone to determine if the proposed vehicle control command 210 iswithin the normal driving behavior safe zone and thus a normal and safecommand. If the proposed vehicle control command 210 is within thenormal driving behavior safe zone, the abnormal command detection module173 has detected a normal or safe proposed vehicle control command 210.In this case, the abnormal command detection module 173 can validate theproposed vehicle control command 210 and pass the validated vehiclecontrol command 220 on to the vehicle control subsystems 146 forexecution. Conversely, if the proposed vehicle control command 210 isnot within the normal driving behavior safe zone, the abnormal commanddetection module 173 has detected an abnormal or unsafe proposed vehiclecontrol command 210. In one embodiment, the abnormal command detectionmodule 173 can simply reject or invalidate the proposed vehicle controlcommand 210 and issue an error or warning alert message 809. In anotherembodiment, the abnormal command detection module 173 can activelymodify the unsafe proposed vehicle control command 210 to produce a safevehicle control command 220 that is most similar to the unsafe proposedvehicle control command 210. In an example embodiment, the abnormalcommand detection module 173 can also issue an error or warning alertmessage 809 when the unsafe proposed vehicle control command 210 ismodified as described herein. The modified and validated vehicle controlcommand 220 can be passed on to the vehicle control subsystems 146 forexecution.

Referring now to FIG. 9, a flow diagram illustrates an exampleembodiment of a system and method 1000 for detection and correction ofan abnormal vehicle control command. The example embodiment can beconfigured for: generating data corresponding to a normal drivingbehavior safe zone (processing block 1010); receiving a proposed vehiclecontrol command (processing block 1020); comparing the proposed vehiclecontrol command with the normal driving behavior safe zone (processingblock 1030); and issuing a warning alert if the proposed vehicle controlcommand is outside of the normal driving behavior safe zone (processingblock 1040).

As used herein and unless specified otherwise, the term “mobile device”includes any computing or communications device that can communicatewith the in-vehicle control system 150 and/or the vehicle control module200 described herein to obtain read or write access to data signals,messages, or content communicated via any mode of data communications.In many cases, the mobile device 130 is a handheld, portable device,such as a smart phone, mobile phone, cellular telephone, tabletcomputer, laptop computer, display pager, radio frequency (RF) device,infrared (IR) device, global positioning device (GPS), Personal DigitalAssistants (PDA), handheld computers, wearable computer, portable gameconsole, other mobile communication and/or computing device, or anintegrated device combining one or more of the preceding devices, andthe like. Additionally, the mobile device 130 can be a computing device,personal computer (PC), multiprocessor system, microprocessor-based orprogrammable consumer electronic device, network PC, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, and the like, and is not limited to portable devices. Themobile device 130 can receive and process data in any of a variety ofdata formats. The data format may include or be configured to operatewith any programming format, protocol, or language including, but notlimited to, JavaScript, C++, iOS, Android, etc.

As used herein and unless specified otherwise, the term “networkresource” includes any device, system, or service that can communicatewith the in-vehicle control system 150 and/or the vehicle control module200 described herein to obtain read or write access to data signals,messages, or content communicated via any mode of inter-process ornetworked data communications. In many cases, the network resource 122is a data network accessible computing platform, including client orserver computers, websites, mobile devices, peer-to-peer (P2P) networknodes, and the like. Additionally, the network resource 122 can be a webappliance, a network router, switch, bridge, gateway, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” can also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Thenetwork resources 122 may include any of a variety of providers orprocessors of network transportable digital content. Typically, the fileformat that is employed is Extensible Markup Language (XML), however,the various embodiments are not so limited, and other file formats maybe used. For example, data formats other than Hypertext Markup Language(HTML)/XML or formats other than open/standard data formats can besupported by various embodiments. Any electronic file format, such asPortable Document Format (PDF), audio (e.g., Motion Picture ExpertsGroup Audio Layer 3—MP3, and the like), video (e.g., MP4, and the like),and any proprietary interchange format defined by specific content sitescan be supported by the various embodiments described herein.

The wide area data network 120 (also denoted the network cloud) usedwith the network resources 122 can be configured to couple one computingor communication device with another computing or communication device.The network may be enabled to employ any form of computer readable dataor media for communicating information from one electronic device toanother. The network 120 can include the Internet in addition to otherwide area networks (WANs), cellular telephone networks, metro-areanetworks, local area networks (LANs), other packet-switched networks,circuit-switched networks, direct data connections, such as through auniversal serial bus (USB) or Ethernet port, other forms ofcomputer-readable media, or any combination thereof. The network 120 caninclude the Internet in addition to other wide area networks (WANs),cellular telephone networks, satellite networks, over-the-air broadcastnetworks, AM/FM radio networks, pager networks, UHF networks, otherbroadcast networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice Over IP (VoIP) networks, metro-area networks, local areanetworks (LANs), other packet-switched networks, circuit-switchednetworks, direct data connections, such as through a universal serialbus (USB) or Ethernet port, other forms of computer-readable media, orany combination thereof. On an interconnected set of networks, includingthose based on differing architectures and protocols, a router orgateway can act as a link between networks, enabling messages to be sentbetween computing devices on different networks. Also, communicationlinks within networks can typically include twisted wire pair cabling,USB, Firewire, Ethernet, or coaxial cable, while communication linksbetween networks may utilize analog or digital telephone lines, full orfractional dedicated digital lines including T1, T2, T3, and T4,Integrated Services Digital Networks (ISDNs), Digital User Lines (DSLs),wireless links including satellite links, cellular telephone links, orother communication links known to those of ordinary skill in the art.Furthermore, remote computers and other related electronic devices canbe remotely connected to the network via a modem and temporary telephonelink.

The network 120 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. The network may also include anautonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of the network may changerapidly. The network 120 may further employ one or more of a pluralityof standard wireless and/or cellular protocols or access technologiesincluding those set forth herein in connection with network interface712 and network 714 described in the figures herewith.

In a particular embodiment, a mobile device 132 and/or a networkresource 122 may act as a client device enabling a user to access anduse the in-vehicle control system 150 and/or the vehicle control module200 to interact with one or more components of a vehicle subsystem.These client devices 132 or 122 may include virtually any computingdevice that is configured to send and receive information over anetwork, such as network 120 as described herein. Such client devicesmay include mobile devices, such as cellular telephones, smart phones,tablet computers, display pagers, radio frequency (RF) devices, infrared(IR) devices, global positioning devices (GPS), Personal DigitalAssistants (PDAs), handheld computers, wearable computers, gameconsoles, integrated devices combining one or more of the precedingdevices, and the like. The client devices may also include othercomputing devices, such as personal computers (PCs), multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PC's, and the like. As such, client devices may range widely interms of capabilities and features. For example, a client deviceconfigured as a cell phone may have a numeric keypad and a few lines ofmonochrome LCD display on which only text may be displayed. In anotherexample, a web-enabled client device may have a touch sensitive screen,a stylus, and a color LCD display screen in which both text and graphicsmay be displayed. Moreover, the web-enabled client device may include abrowser application enabled to receive and to send wireless applicationprotocol messages (WAP), and/or wired application messages, and thelike. In one embodiment, the browser application is enabled to employHyperText Markup Language (HTML), Dynamic HTML, Handheld Device MarkupLanguage (HDML), Wireless Markup Language (WML), WMLScript, JavaScript™,EXtensible HTML (xHTML), Compact HTML (CHTML), and the like, to displayand send a message with relevant information.

The client devices may also include at least one client application thatis configured to receive content or messages from another computingdevice via a network transmission. The client application may include acapability to provide and receive textual content, graphical content,video content, audio content, alerts, messages, notifications, and thelike. Moreover, the client devices may be further configured tocommunicate and/or receive a message, such as through a Short MessageService (SMS), direct messaging (e.g., Twitter), email, MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like. The client devices may alsoinclude a wireless application device on which a client application isconfigured to enable a user of the device to send and receiveinformation to/from network resources wirelessly via the network.

The in-vehicle control system 150 and/or the vehicle control module 200can be implemented using systems that enhance the security of theexecution environment, thereby improving security and reducing thepossibility that the in-vehicle control system 150 and/or the vehiclecontrol module 200 and the related services could be compromised byviruses or malware. For example, the in-vehicle control system 150and/or the vehicle control module 200 can be implemented using a TrustedExecution Environment, which can ensure that sensitive data is stored,processed, and communicated in a secure way.

FIG. 10 shows a diagrammatic representation of a machine in the exampleform of a computing system 700 within which a set of instructions whenexecuted and/or processing logic when activated may cause the machine toperform any one or more of the methodologies described and/or claimedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a web appliance, a set-top box (STB), a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g.,a System-on-a-Chip (SoC), general processing core, graphics core, andoptionally other processing logic) and a memory 704, which cancommunicate with each other via a bus or other data transfer system 706.The mobile computing and/or communication system 700 may further includevarious input/output (I/O) devices and/or interfaces 710, such as atouchscreen display, an audio jack, a voice interface, and optionally anetwork interface 712. In an example embodiment, the network interface712 can include one or more radio transceivers configured forcompatibility with any one or more standard wireless and/or cellularprotocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th(4G) generation, and future generation radio access for cellularsystems, Global System for Mobile communication (GSM), General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), WidebandCode Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, WirelessRouter (WR) mesh, and the like). Network interface 712 may also beconfigured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth©, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication and data processing mechanismsby which information/data may travel between a computing system 700 andanother computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

What is claimed is:
 1. A system comprising: a data processor; and avehicle control module, executable by the data processor, the vehiclecontrol module being configured to perform a vehicle control commandvalidation operation for autonomous vehicles, the vehicle controlcommand validation operation being configured to: receive a proposedvehicle control command; compare the proposed vehicle control commandwith data corresponding to a normal driving behavior; and modify theproposed vehicle control command to produce a modified and validatedvehicle control command if the proposed vehicle control command does notcomply with the normal driving behavior data.
 2. The system of claim 1being further configured to capture data through vehicle sensorsubsystems and driving simulation data to model typical human drivingbehaviors.
 3. The system of claim 1 being further configured to plotdata corresponding to typical human driving behaviors on a graph.
 4. Thesystem of claim 1 being further configured to determine a distance froma point on a graph corresponding to the proposed vehicle control commandto a nearest point on or within a normal driving behavior safe zone. 5.The system of claim 1 being further configured to replace the proposedvehicle control command with a modified vehicle control commandcorresponding to a point, which is the closest point of a normal drivingbehavior safe zone to the proposed vehicle control command.
 6. Thesystem of claim 1 wherein a normal driving behavior safe zone isconfigured to retain information corresponding to human drivingbehaviors as mathematical or data representations.
 7. The system ofclaim 1 wherein the modified and validated vehicle control command isoutput to a vehicle control subsystem causing the autonomous vehicle tofollow a trajectory corresponding to the modified and validated vehiclecontrol command.
 8. A method to perform vehicle control commandvalidation for autonomous vehicles, the method comprising: receiving aproposed vehicle control command by use of a data processor; comparingthe proposed vehicle control command with data corresponding to a normaldriving behavior; and modifying the proposed vehicle control command toproduce a modified and validated vehicle control command if the proposedvehicle control command does not comply with the normal driving behaviordata.
 9. The method of claim 8 including capturing data through vehiclesensor subsystems and driving simulation data to model typical humandriving behaviors.
 10. The method of claim 8 including plotting datacorresponding to typical human driving behaviors on a graph.
 11. Themethod of claim 8 including determining a distance from a point on agraph corresponding to the proposed vehicle control command to a nearestpoint on or within a normal driving behavior safe zone.
 12. The methodof claim 8 including replacing the proposed vehicle control command witha modified vehicle control command corresponding to a point, which isthe closest point of a normal driving behavior safe zone to the proposedvehicle control command.
 13. The method of claim 8 wherein a normaldriving behavior safe zone is configured to retain informationcorresponding to human driving behaviors as mathematical or datarepresentations.
 14. The method of claim 8 wherein the modified andvalidated vehicle control command is output to a vehicle controlsubsystem causing the autonomous vehicle to follow a trajectorycorresponding to the modified and validated vehicle control command. 15.A non-transitory machine-useable storage medium embodying instructionswhich, when executed by a machine, cause the machine to: receive aproposed vehicle control command; compare the proposed vehicle controlcommand with data corresponding to a normal driving behavior; and modifythe proposed vehicle control command to produce a modified and validatedvehicle control command if the proposed vehicle control command does notcomply with the normal driving behavior data.
 16. The non-transitorymachine-useable storage medium of claim 15 wherein the instructions arefurther configured to capture data through vehicle sensor subsystems anddriving simulation data to model typical human driving behaviors. 17.The non-transitory machine-useable storage medium of claim 15 whereinthe instructions are further configured to determine a distance from apoint on a graph corresponding to the proposed vehicle control commandto a nearest point on or within a normal driving behavior safe zone. 18.The non-transitory machine-useable storage medium of claim 15 whereinthe instructions are further configured to use data mining techniques toproject aggregated human driver behavior sensor data from a highdimensional space to a low dimensional space.